Booij, Martijn J.

Clements, M.

Coxon, G.

Freer, J.

Pianosi, F.

Wagener, T.

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Defining prior probabilities for hydrologic model structures in UK catchments

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Geophysical Research AbstractsVol. 16, EGU2014-12481, 2014EGU General Assembly 2014© Author(s) 2014. CC Attribution 3.0 License.Defining prior probabilities for hydrologic model structures in UKcatchmentsMichiel Clements (1,4), Francesca Pianosi (2), Thorsten Wagener (2), Gemma Coxon (3), Jim Freer (3), andMartijn Booij (4)(1) Delft University of Technology, The Netherlands, (2) Department of Civil Engineering, University of Bristol, UnitedKingdom, (3) School of Geographical Sciences, University of Bristol, United Kingdom, (4) Department of Civil Engineering,University of Twente, The NetherlandsThe selection of a model structure is an essential part of the hydrological modelling process. Recently flexiblemodeling frameworks have been proposed where hybrid model structures can be obtained by mixing togethercomponents from a suite of existing hydrological models. When sufficient and reliable data are available,this framework can be successfully utilised to identify the most appropriate structure, and associated optimalparameters, for a given catchment by maximizing the different models ability to reproduce the desired range offlow behaviour. In this study, we use a flexible modelling framework to address a rather different question: canthe most appropriate model structure be inferred a priori (i.e without using flow observations) from catchmentcharacteristics like topography, geology, land use, and climate? Furthermore and more generally, can we definepriori probabilities of different model structures as a function of catchment characteristics?To address these questions we propose a two-step methodology and demonstrate it by application to a na-tional database of meteo-hydrological data and catchment characteristics for 89 catchments across the UK. Inthe first step, each catchment is associated with its most appropriate model structure. We consider six possiblestructures obtained by combining two soil moisture accounting components widely used in the UK (Penman andPDM) and three different flow routing modules (linear, parallel, leaky). We measure the suitability of a modelstructure by the probability of finding behavioural parameterizations for that model structure when applied to thecatchment under study. In the second step, we use regression analysis to establish a relation between selectedmodel structures and the catchment characteristics. Specifically, we apply Classification And Regression Trees(CART) and show that three catchment characteristics, the Base Flow Index, the Runoff Coefficient and the meanDrainage Path Slope, can be used to predict which model structure is more appropriate.The study constitutes a first step to enhance the choice of model structures in hydrological modeling acrossregions, with potentially interesting applications for predictions in ungauged basins, that was made possible bythe analyses of large datasets.View publication stats

Defining prior probabilities for hydrologic model structures in UK catchments

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